Machine learning based fraud detection For E-Commerce

Nikita Verma; Kshiti Uboveja; Manoj Kumar Singh

doi:10.59367/ijfiest.v2i1.13

Authors

Nikita Verma Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur Chhattisgarh, India
Kshiti Uboveja Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur Chhattisgarh, India
Manoj Kumar Singh Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur Chhattisgarh, India

DOI:

https://doi.org/10.59367/ijfiest.v2i1.13

Keywords:

Decision Tree, Machine Learning, Frauds,classification

Abstract

Since consumers first started conducting business online, frauds in e-commerce have been on the rise. People are vulnerable to harmful attacks because they readily divulge their personal information to strangers. Hacking is used to carry out these nefarious actions. A hacker is someone who uses the internet to access another person's private information in order to steal their money with only one click. We can employ machine learning-based techniques to stop this, such as supervised decision trees that classify data on fraudulent and legitimate transactions after being given it. When a tree is broken into child nodes, the fraudulence score calculation begins at the root node; other nodes are also divided.

Author Biographies

Nikita Verma, Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur Chhattisgarh, India
Kshiti Uboveja, Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur Chhattisgarh, India
Manoj Kumar Singh, Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur Chhattisgarh, India

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